The results indicate that an induced latent tag set as a whole increases parsing performance.

not every split made by the HMM-LA seems to be useful for the parser.


We also tried Jiang and Zhai's subset selection technique ( §3.1 in Jiang and Zhai (2007)), which assumes labeled training material for the target domain.


As a result, a system must be prepared to handle disfluencies, utterance fragments, and other phenomena that are entirely grammatical in speech, but not in writing.

a system designed for transcripts of speech does not need to identify errors specific to written language such as punctuation or spelling mistakes.


Hassanali and Liu's system was designed for transcripts of spoken language collected from children with impaired language, and is able to detect the set of errors they defined very well.

it cannot be straightforwardly adapted to novel error sets.


CCG's generalized notion of constituency means that many derivations are possible for a given a set of lexical categories.

most of these derivations will be semantically equivalent-for example, deriving the same dependency structures-in which case the actual choice of derivation is unimportant.


Auli and Lopez (2011b) find that A * CCG parsing with this heuristic is very slow.

they achieve a modest 15% speed improvement over CKY when A * is combined with adaptive supertagging.


If we assume all derivations licensed by our grammar are equally likely, and that lexical category assignments are conditionally independent given the sentence, we can compute the optimal parseŷ as: As discussed in Section 2.1, many derivations are possible given a sequence of lexical categories, some of which may be semantically distinct.

our model will assign all of these an equal score, as they use the same sequence of lexical categories.


Unlike the other methods, this approach does affect the probabilities which are calculated, as the normalizing constant is only computed for a subset of the categories.

the probability mass contained in the pruned categories is small, and it only slightly decreases parsing accuracy.


Our experiments show that the parser achieves over 80% unlabeled attachment accuracy on our new, high-quality test set and measure the benefit of our contributions.

to the edited, standardized language of traditional publications such as news reports, social media text closely represents language as it is used by people in their everyday lives.


These informal texts, which account for ever larger proportions of written content, are of considerable interest to researchers, with applications such as sentiment analysis (Greene and Resnik, 2009;Kouloumpis et al., 2011).

their often nonstandard content makes them challenging for traditional NLP tools.


A lot of work has gone into developing powerful optimization methods for solving these combinatorial problems.

we explore, analyze, and demonstrate that a substantially simpler randomized greedy inference algorithm already suffices for near optimal parsing: a) we analytically quantify the number of local optima that the greedy method has to overcome in the context of first-order parsing; b) we show that, as a decoding algorithm, the greedy method surpasses dual decomposition in second-order parsing; c) we empirically demonstrate that our approach with up to third-order and global features outperforms the state-of-the-art dual decomposition and MCMC sampling methods when evaluated on 14 languages of non-projective CoNLL datasets.


In fact, drawing the initial tree uniformly at random results in the same performance as when initialized from a trained first-order distribution.

sufficient randomization of the starting point is critical.


Therefore, we could initialize the tree y (0) with a tree from a first-order parser, or draw the initial tree from a first-order distribution other than uniform.

perhaps surprisingly, as we demonstrate later, little is lost with uniform initialization.


use the grammar of pregroups as the syntactic machinery to construct distributional meaning representations, since both pregroups and vector spaces can be seen as examples of the same abstract structure, which leads to a particularly clean mathematical description of the compositional process.

the approach applies more generally, for example to other forms of categorial grammar, such as Combinatory Categorial Grammar (Steedman, 2000;Maillard et al., 2014), and also to phrase-structure grammars in a way that a formal linguist would recognize (Baroni et al., 2014).


predicting implausible when the gold standard label is plausible).

tensor produces almost equal numbers of false positives and false negatives, but sometimes produces false negatives with low frequency nouns (e.g.


Besides them, German is the only language to which Romanian drew closer (a possible explanation might be the fact that, after the establishment of Germans in Banat and Transylvania, many German words entered the basic Romanian lexicon).

the similarity between Romanian and almost all the Slavic languages decreased in the same period.


This selection was likely made to avoid the noise of learning multiple senses for infrequent words.

our method is robust to noise, which can be seen by the good performance of our model that learns multiple embeddings for the top 30,000 most frequent words.


Knowledge graphs are recently used for enriching query representations in an entity-aware way for the rich facts organized around entities in it.

few of the methods pay attention to nonentity words and clicked websites in queries, which also help conveying user intent.


Query understanding is the process of generating a representation which characterizes a user's search intent (Croft et al., 2010), which is of vital importance for information retrieval.

users are remarkably laconic in describing their information needs due to anomalous state of knowledge (Belkin et al., 1982), resulting in vague and underspecified queries, which makes it especially difficult to understand and locate what they intended for in mountains of web data.


A widely accepted way to use knowledge graph is tying queries with it by annotating entities in them, also known as entity linking.

information need is conveyed through more than entities.


We do not split queries into clusters or subtopics relevant to the original query to indicate a intent, but link them in an graph with intent feature similarity, weakly or strongly, in a holistical view.

previous research can be categorized by what kind of resources they rely on.


Markov logic networks combine Markov networks with first-order logic in a probabilistic framework (Richardson and Domingos, 2006) where φ i is a first order formula and w i is the penalty (the formula's weight).

to the first-order logic, whereby a formula represents a hard constraint, these logic formulas are relaxed and can be violated with penalties in the MLN.


The system most similar to ours is DEANNA (Yahya et al., 2012).

dEANNA extracts predicate-argument structures from the questions using three hand-written patterns.


A  large corpus may be used to build relation expression models , but not as supporting evidence for target entities.

the Web and IR community generally assumes a free-form query that is often telegraphic (Guo et al., 2009;Sarkas et al., 2010;Li et al., 2011).


In all cases and for all metrics, using the corpus and KG together gives superior performance to using any of them alone.

it is instructive that in case of TREC-INEX, corpusonly is better than KG-only, whereas this is reversed for WQT, which also supports the above argument.


Previous sections discussed estimating difficulty scores of resolved questions, from which pairwise competitions could be extracted.

for newly posted questions without any answers received, no competitions could be extracted and none of the above methods work.


In the task of predicting reading difficulty levels, documents targeting different grade levels are taken as ground truth, which can be easily obtained from the web.

there is no naturally annotated data for our QDE task on the web.


In this case, the lexica outperformed previous results for gender prediction of Twitter users, which ranged from 75.5% to 87% (Burger et al., 2011;Ciot et al., 2013;Liu and Ruths, 2013;Al Zamal et al., 2012).

the lexica were unable to match the 92.0% accuracy Burger et al.


Our method is also an emotion-based method.

our approach is different from existing emotion-based methods in the following aspects.


Germesin and Wilson (2009) also showed accuracies of 98% in detecting agreement in the AMI corpus using lexical, subjectivity and dialogue act features.

they note that their system could not classify disagreement accurately due to the small number of training examples in this category.


Referrals and questions, as well as polarity measures in the first section of the post, were found to be most useful.

their analysis did not  Table 1: Characteristics of the four categories determined from the crowd-sourced annotation.


show that such inferences may be exploited to significantly improve explicit sentiment analysis systems.

to achieve its results, the system developed by  requires that all instances of +/-effect events in the corpus be manually provided as input.


Several works such as Hatzivassiloglou and McKeown (1997), Turney and Littman (2003), Kim and Hovy (2004), Strapparava and Valitutti (2004), and Peng and Park (2011) have tackled automatic lexicon expansion or acquistion.

in most such work, the lexicons are word-level rather than sense-level.


With only hierarchical information (i.e., hypernym (H) and troponym (T) relations), it already shows good performance for all classes.


By gloss similarity, many nodes are connected to each other.

since uncertain connections can cause incorrect propagation in the graph, this negatively affects the performance.


Note that our method is similar to Active Learning (Tong and Koller, 2001), in that both automatically identify which unlabeled instances the human should annotate next.

in active learning, the goal is to find instances that are difficult for a supervised learning system.


It would be promising to combine our method with other methods to enable it to find +effect and -effect senses that are outside the coverage of WordNet.

a WordNet-based lexicon gives a substantial base to build from.


To tackle this problem, models in the literature usually use some seed words for each sentiment topic to define Dirichlet priors with asymmetric concentration parameter vectors (Sauper et al., 2011;Kim et al., 2013), or use seed words to initialize word assignment to sentiment topic (Lin and He, 2009), or both Jo and Oh, 2011).

these seed words are usually arbitrarily selected, and how to define asymmetric priors is not clear, especially when we would like to capture more than two (positive and negative) kinds of sentiments.


Sentiment association: The sentiment label takes R values, and there are T different values for the polarity score in the sentiment lexicon.

the relation between sentiment labels and polarity scores are unknown.


There may be better methods to use seed words for aspect discovery (Jagarlamudi et al., 2012;Mukherjee and Liu, 2012), and it would be interesting to combine their methods with ours.

this is beyond the scope of this paper, and we list it as future work.


Finally, we assume each phrase is associated with one latent aspect.


For example, if we learn that the hashtag #lovelife is associated with JOY, then we can extract the phrase "love life" from the hashtag and use it to recognize emotion in the body of tweets.

unlike hashtags, which are selfcontained, the words surrounding a phrase in a tweet must also be considered.


Each phrase is assumed to express the same emotion as the original hashtag.

as we will see in Section 4, just the presence of a phrase yields low precision, and surrounding context must also be taken into account.


Some previous researches used the URL similarity or patterns to find parallel page pairs.

due to the diversity of web page styles and website maintenance mechanisms, bilingual websites adopt varied naming schemes for parallel documents (Shi, et al, 2006).


Carl (2010) showed that expert translators tend to adopt local planning: they read a few words ahead and then translate in a roughly online fashion.

word order differences between languages will necessarily require longer range planning and movement.


Conventional incremental MT learning experiments typically resemble domain adaptation: smallscale baselines are trained and tuned on mostly outof-domain data, and then re-tuned incrementally on in-domain data.


The process study most similar to ours is that of Koehn (2009a), who compared scratch, post-edit, and simple interactive modes.

he used undergraduate, non-professional subjects, and did not consider re-tuning.


Many research translation UIs have been proposed including TransType (Langlais et al., 2000), Caitra (Koehn, 2009b), Thot (Ortiz-Martínez and Casacuberta, 2014), TransCenter (Denkowski et al., 2014b), and CasmaCat (Alabau et al., 2013).

to our knowledge, none of these interfaces were explicitly designed according to mixedinitiative principles from the HCI literature.


The maximum phrase length (mpl) introduces in g (0) more configurations of reordering constraints ([l, C] in Figure 3).

not many more, due to C being limited by the distortion limit d. In practice, we observe little impact on time performance.


derivations in the vast majority of the cases (100% with a 3-gram LM) and translation quality in terms of BLEU is no different from OS * .

with k < 10 4 both model scores and translation quality can be improved.


2 through sparse indicator features over phrase pairs instead, but prior work with such models still relies on word aligned corpora for estimation (Xiong et al., 2006;Nguyen et al., 2009).

recent evaluations of the approach show little gain over the simpler frequency-based estimation method (Cherry, 2013).


6 The feature set closest to Cherry (2013) is SparseHRM.

while Cherry had to severely restrict his features for batch lattice MIRA-based training, our maximum expected BLEU approach can handle millions of features.


Previous research has shown that directly training a reordering model for BLEU can vastly outperform a likelihood trained maximum entropy reordering model (Cherry, 2013).

the two approaches do not only differ in the objectives used, but also in the type of training data.


The standard configuration of modern phrasebased Statistical Machine Translation (SMT) (Koehn et al., 2003) systems can produce very acceptable results on some tasks.

early integration of better features to guide the search for the best hypothesis can result in significant improvements, an expression of the complexity of modeling translation quality.


For instance, improvements have been obtained by integrating features into decoding that better model semantic coherence at the sentence level (Hasan and Ney, 2009) or syntactic well-formedness (Schwartz et al., 2011).

early use of such complex features typically comes at a high computational cost.


(2007) described a greedy search decoder, first introduced in (Germann et al., 2001), able to improve translations produced by a dynamic programming decoder using the same scoring function and translation table.


In both scenarios our lexicon is 60-70% smaller.

to the development results, single sentence performance decreases slightly compared to Artzi and Zettlemoyer (2013b).


The templates presented so far model grammatically correct input.

in dialogue domains such as ATIS, speakers often omit words.


This text-to-code problem may be thought of as a machine translation (MT) problem, where one aims to translate sentences in English to the formal language of LSCs.

standard statistical MT techniques rely on the assumption that textual requirements and code are aligned at a sentence level.


By choosing only a few such principle axes, we can represent the data in a lower dimensional space.

t-SNE embedding performs a non-linear dimensionality reduction preserving the local structures.


These kinds of corruptions are expected to be more frequently harmful, at least for languages with relatively rigid word order.

there may still be certain transpositions that are benign, at least for grammaticality.


Again, we argue that a system can improve on this by predicting unseen parts of the sentence to find a better tradeoff between these conflicting goals.

to evaluate and optimize such a system, we must measure where a system falls on the continuum of accuracy versus expeditiousness.


Our goal is to learn a classifier that can accurately mimic the oracle's choices on previously unseen data.

at test time, when we run the learned policy classifier, the learned policy's state distribution may deviate from the optimal policy's state distribution due to imperfect imitation, arriving in states not on the oracle's path.


In the complete version of searn, the cost of each action is calculated as the highest expected reward starting at the current state minus the actual roll-out reward.

computing the full roll-out reward is computationally very expensive.


Any linguist would agree that these sequences are substitutable (in fact, they have lots of local contexts in common).

statistical evidence points otherwise, since their context distributions are not close enough.


For instance, using 5-grams based only on the 26 letters of the English alphabet will result in a feature space of 26 5 = 11, 881, 376 features.

in the experiments presented in this work the feature space includes 5-grams along with 6-grams, 7-grams and 8-grams.


Diving into details, it can be observed that the results obtained by KRR are higher than those obtained by KDA.

both methods perform very well compared to the state of the art.


We may want to measure their English vocabulary with an emphasis on computer science rather than their general English vocabulary.

such an extension is impossible via current methods, and thus it is desirable to sample algorithms to be able to handle domain specificity.


For our applications, this means that the vocabulary tests for vocabulary prediction must always be computerized.

noninteractive algorithms allow us to have vocabulary tests printed in the form of handouts, so we focus on non-interactive algorithms throughout this paper.


In the usual label propagation setting, the "test" nodes (data) are prepared separately from the training nodes to determine how accurately the algorithm can classify forthcoming or unseen nodes.

in our setting, there were no such forthcoming words.


Another alternative method is Information Gain (Yang and Pedersen, 1997).

as defined in equation 2, it measures the entropy gain associated with feature t in assigning the class label c. these methods are limited: they do not provide ranked lists per-L1 class, and more importantly, they do not explicitly capture underuse.


The verbs in LVCs are assumed to be flexible for inflection (Baldwin and Kim, 2010).

we know little about how fineteness contributes to the formation of LVCs.


That is, previous studies focused only on text printed on paper.

with the increasing use of hand-held devices, people in these days use various reading devices such as a tablet and a smart phone as well as a paper.


According to their results, the average number of verb phrases in a sentence, the number of words in an article, the likelihood of the vocabulary, and the likelihood of the discourse relations are highly correlated with human ratings.

these studies did not consider the reading devices, but focused on how well a text is written.


The large number of noun phrases in a text requires a reader to remember more items (Barzilay and Lapata, 2008;Pitler and Nenkova, 2008).

it also makes the text more interesting.


In other words, English abbreviation generation is based on words in the full form.

in Chinese, word is not the most suitable abbreviating unit.


VerbNet and WordNet) are used to generalize structured event features in order to reduce their sparseness.

the problem of accurately predicting stock price movement using structured events is challenging, since events and the stock market can have complex relations, which can be influenced by hidden factors.


Intuitively, three or more hidden layers may achieve better performance.

three hidden layers mean that we construct a five-layer deep neural network, which is difficult to train (Bengio et al., 1994).


These studies primarily use bags-of-words to represent financial news documents.

as Schumaker and Chen (2009) and Xie et al.


Imposing minimum node and edge frequencies in the co-occurrence graph was also tested.

applying no thresholds provided the highest average coherence.


Whereas many solutions in NLP (including topic models) require document segmentation, lexical normalization and statistical normalizations on the co-occurrence matrix itself, the only variable in our method is the cooccurrence window size.

lemmatization (or stemming) could help collapse morphosyntactic variation among terms in the results, but stop-word removal, sentence segmentation and TF-IDF weighting appear unnecessary.


One way to measure the importance of a community would be to use significance testing on the internal link mass compared to the external (Csardi and Nepusz, 2006).

this approach discards some factors for which one might want to account, such as centrality in the network of communities and their composition.


Recently, Wan and Xiao (2008) proposed a model that incorporates a local neighborhood of a document.

their neighborhood is limited to textually-similar documents, where the cosine similarity between the tf-idf vectors of documents is used to compute their similarity.


Nguyen and Kan (2007) extended KEA to include features such as the distribution of keyphrases among different sections of a research paper, and the acronym status of a term.

to these works, we propose novel features extracted from the local neighborhoods of documents available in interlinked document networks.


(2009) extended KEA as well to integrate information from Wikipedia.

we used only information intrinsic to our data.


(2010), where a set of important keyphrases is extracted first from the citation contexts in which the paper to be summarized is cited by other papers and then the "best" subset of sentences that contain such keyphrases is returned as the summary.

keyphrases in (Qazvinian et al., 2010) are extracted using frequent n-grams in a language model framework, whereas in our work, we propose a supervised approach to a different task: keyphrase extraction.


Hence, similar to (Hulth, 2003;Mihalcea and Tarau, 2004;Liu et al., 2009), we did not use the entire text of a paper.

extracting keyphrases from sections such as "introduction" or "conclusion" needs further attention.


For this reason, we used the contexts provided by CiteSeer x directly.

in future, it would be interesting to incorporate in our models more sophisticated approaches to identifying the text that is relevant to a target citation (Abu-Jbara and Radev, 2012;Teufel, 1999) and study the influence of context lengths on the quality of extracted keyphrase.


For example, it is difficult to distinguish true semantic similarity (e.g., "cows" -"cattle") from mere associational relatedness (e.g., "cows" -"milk") based on cooccurrence statistics.

coreference chains should be able to make that distinction since only "cows" and "cattle" can occur in the same coreference chain, not "cows" and "milk".


The pattern-based approach (Lin et al., 2003;Turney, 2008) discussed above also needs few resources.

to our work, it relies on patterns and might therefore restrict the number of recognizable synonyms and antonyms to those appearing in the context of the pre-defined patterns.


That is, if many of the character n-grams of a fragment are infrequent in the document, it would be probably a plagiarized fragment.

if many of them are frequent, then the fragment is likely to be original.


Note that, what we explained above is solely how to compute the class of each n-gram of a document.

our purpose is to represent the document fragments using these classes.


Several recent papers on Arabic dialect identification have hinted that using a word unigram model is sufficient and effective for the task.

most previous work was done on a standard fairly homogeneous dataset of dialectal user comments.


We used the trained model to segment the training and test sets.

• Morphological Rules: to Morfessor, we developed only 15 morphological rules (based on the analysis proposed in Section 3) to segment ARZ text.


Combining S mrph and S rule features with the S lex feature led to further improvement.

as shown in Table 2, using the S lex feature alone with the MAN and VERB lists led to the best results (94.6%), outperforming using all other features either alone or in combination.


Finally, the enhanced query model (that is P(w|H) in speech recognition) can be estimated by RM, SMM, RSMM or QMM, and further combined with the background n-gram (e.g., trigram) language model to form an adaptive language model to guide the speech recognition process.

extractive speech summarization aims at producing a concise summary by selecting salient sentences or paragraphs from the original spoken document according to a predefined target summarization ratio (Carbonell and Goldstein, 1998;Mani and Maybury, 1999;Nenkova and McKeown, 2011;Liu and Hakkani-Tur, 2011).


Another finding was that the candidates' power correlates with the distribution of topics they speak about in the debates: candidates with more power spoke significantly more about certain topics (e.g., economy) and less about certain other topics (e.g., energy).

these findings relate to the specific election cycle that was analyzed and will not carry over to political debates in general.


We also investigated the utility of topic shifting features described in (Prabhakaran et al., 2014) extracted using LDA based topic modeling.

they did not improve the performance of the ranker, and hence we do not discuss them in detail in this paper.


Thus the PLRE improvement is small for order = 2, but more substantial for order = 3.

for the Small-Russian dataset, the vocabulary size is much larger and consequently the bigram counts are sparser.


An alternate technique is to use word-classing (Goodman, 2001;Mikolov et al., 2011), which can reduce the cost of exact normalization to O( √ V ).

our approach is much more scalable, since it is trivially parallelized in training and does not require explicit normalization during evaluation.


This places the trigger from the question as the source of the query path (see both queries in the bottom right portion of the running example).

had the verb been require, the trigger would be the target of the query.


Instead it just sets them to zero at the beginning and uses the pivot slice to re-calculate them.

our method of BPTF is well suited to symmetric relations with many unknown relatedness entries.


The vocabulary of the antonym entries in the thesaurus is limited, and does not contain many words in the antonym questions.

distributional similarities can be trained from large corpora and hence have a large coverage for words.


Our model usually takes less than 30 minutes to meet the convergence criteria (on a machine with an Intel Xeon E3-1230V2 @ 3.3GHz CPU ).

the MRLSA requires about 3 hours for tensor decomposition (Chang et al., 2013).


(6) would exhibit the exchange symmetry if not for the log(X i ) on the right-hand side.

this term is independent of k so it can be absorbed into a bias b i for w i .


At first glance this might seem like a substantial improvement over the shallow windowbased approaches, which scale with the corpus size, |C|.

typical vocabularies have hundreds of thousands of words, so that |V | 2 can be in the hundreds of billions, which is actually much larger than most corpora.


The context of a word is often defined as the words appearing in a window of fixed-length (bag-of-words) and a simple approach is to treat the co-occurrence statistics of a word w as a vector representation for w (Mitchell and Lapata, 2008;Mitchell and Lapata, 2010); alternatively, dependencies between words can be used to define contexts (Goyal et al., 2013;Erk and Padó, 2008;Thater et al., 2010).

to distributional representations, NNLMs represent words in a low-dimensional vector space (Bengio et al., 2003;Collobert et al., 2011).


6This is equivalent to a softmax regression model.

when the vocabulary V is large, computing the softmax function in Eq.


The scores ρ = 0.51 for the NN task and ρ = 0.48 for the VO task are the best results to date.

the score ρ = 0.34 for the SVO task did not improve by increasing the dimensionality.


The traditional approach is based on the assumption that every mention of an entity pair (e.g., Obama and USA) participates in the known relation between the two (i.e., born in).

this introduces noise, as not every mention expresses the relation we are assigning to it.


Other than using the existing known facts to label the text corpora in a distant supervision setting (Bunescu and Mooney, 2007;Mintz et al., 2009;Riedel et al., 2010;Ritter et al., 2013), an existing knowledge base is typically not involved in the process of relation extraction.

this paradigm has started to shift recently, as researchers showed that by taking existing facts of a knowledge base as an integral part of relation extraction, the model can leverage richer information and thus yields better performance.


(2011) made the observation that if λ = 0, the matrix inversion can be calculated by Then, it only involves an inversion of an r × r matrix, namely A T A.


Using this approach, the task of relation extraction can easily be scaled to hundreds of different relationships.

distant supervision leads to a challenging multiple instance, multiple label learning problem.


Thus, the vector of an entity may encode global information from the entire graph, and hence scoring a candidate fact by designed vector operations plays a similar role to long range "reasoning" in the graph.

since this requires the vectors of both entities to score a candidate fact, this type of methods can only complete missing facts for which both entities exist in the knowledge graph.


Moreover, the number of matched Wikipedia anchors (∼40M) is relatively small compared to the total number of word pairs (∼2.0B in Wikipedia) and hence the contribution is limited.

the advantage is that the quality of the data is very high and there are no ambiguity/completeness issues.


Observing the scores assigned to true triplets by TransE, we notice that triplets of popular relations generally have larger scores than those of rare relations.

pTransE, as a probabilistic model, assigns comparable scores to true triplets of both popular and rare relations.


Most existing works on sentiment summarization focus on predicting the overall rating on an entity (Pang et al., 2002;Pang and Lee, 2004) or estimating ratings for product features (Lu et al., 2009;Lerman et al., 2009;Snyder and Barzilay, 2007;Titov and McDonald, 2008)).

the opinion summaries in such systems are extractive, meaning that they generate a summary by concatenating extracts that are representative of opinion on the entity or its aspects.


Starlet-H uses extractive summarization techniques to select salient quotes from the input reviews and embeds them into the abstractive summary to exemplify, justify or provide evidence for the aggregate positive or negative opinions.

starlet-H assumes a limited number of aspects as input and needs a large amount of training data to learn the ordering of aspects for summary generation.


Highlighting the reasons behind opinions in reviews was also previously proposed in (Kim et al., 2013).

their approach is extractive and similar to (Ganesan et al., 2010) does not cover the distribution of opinions.


As an alternative we could have selected connectives based on the discourse relations specified in the aspects tree.


For aspect phrase 1, it seems that the sentiment distribution is consistent with that of the left aspect.

we can not say that the phrase belongs to the aspect because the distribution may be the same for two different aspects.


For each supervised model, we provide a proportion of manually labeled data for training, which is randomly selected from goldstandard annotations.

we didn't use any labeled data for our approach.


For example, multi-document summarization of news articles aims at synthesizing contents of similar news and removing the redundant information contained by the different news articles.

each scientific paper has much specific content to state its own work and contribution.


There are several NLP systems whose accuracy depends crucially on finding misspellings fast.

the classical approach is based on a quadratic time algorithm with 80% coverage.


Any accurate systems, such as the ones developed for cross document coreference, text similarity, semantic search or digital humanities, should be able to handle the misspellings in corpora.

the issue is not easy and the required processing time, memory or the dependence on external resources grow fast with the size of the analyzed corpus; consequently, most of the existing algorithms are inefficient.


For this reason the ED algorithm is the most common way to detect and correct the misspellings.

there is a major inconvenience associated with the use of ED, namely, ED runs in quadratic time considering the length of the strings, O(n 2 ).


The CDC system computes the probability of coreference for two mentions t and t' using a similarity metrics into a vectorial space, where vectors are made out of contextual features occurring with t and t' respectively (Grishman, 1994).

the information extracted from documents is often too sparse to decide on coreference (Popescu, 2009).


Thus, missing a coreference pair may result in losing the possibility of realizing further coreferences.

for two mentions matching a misspelling pattern which is highly accurate, the threshold for contextual evidence is lowered.


Nevertheless, noisy WA makes both analyzing WS and improving SMT quality quite hard.

by using manual WA, we can clearly analyze the segmentation problems (Section 2), and train supervised models to solve the problem (Section 3).


The direct translation model trained with the standard bilingual corpus exceeds in translation performance, but its weakness lies in low phrase coverage.

the pivot model has characteristics characters.


Synthetic Method: It aims to create a synthetic source-target corpus by: (1) translate the pivot part in source-pivot corpus into target language with a pivot-target model; (2) translate the pivot part in pivot-target corpus into source language with a pivot-source model; (3) combine the source sentences with translated target sentences or/and combine the target sentences with translated source sentences (Utiyama et al., 2008;Wu and Wang, 2009).

it is difficult to build a high quality translation system with a corpus created by a machine translation system.


The ability to make context-sensitive translation decisions is one of the major strengths of phrasebased SMT (PSMT).

the way PSMT exploits source-language context has several limitations as pointed out, for instance, by Quirk and Menezes (2006) and Durrani et al.


Arabic-English results did not reveal statistically significant differences between the two distortion limits for Pos→Pos→Pos•Pos.

for Lex•Lex BLEU decreases when using a distortion limit of 10 compared to a limit of 5.


RI, thus, significantly enhances the computational complexity of deriving a VSM from text.

the application of the RI technique (likewise the standard truncated SVD in LSA) is limited to 2 normed spaces, i.e.


In order to employ the RMI method for the construction of a VSM at reduced dimension and the estimation of the 1 distance between vectors, two model parameters should be decided: (a) the targeted (reduced) dimensionality of the VSM, which is indicated by m in Equation 8 and (b) the number of non-zero elements in index vectors, which is determined by s in Equation 6.

to the classic one-dimension-per-context-element methods of VSM construction, 10 the value of m in RPs and thus in RMI is chosen independently of the number of context elements in the model (n in Equation 8).


These vectors must be stored and accessed efficiently when using the RMI technique.

resources that are required for the storage and processing of floating numbers is high.


The bottom-right plot shows the cluster of phrases that are semantically similar (countries or regions).

the top-right plot shows the phrases that are syntactically similar.


It is common practice to directly use the type value of each variable as an index and maintain a set of sites for each type.

maintaining a (r 1 , r 2 , r 3 ) triple for each node in the chosen tree is too memory heavy in our appli-cation.


As each tree fragment sampled from the forest represents a unique translation rule, we do not need to explicitly extract the rules; we merely need to collect them and count them.

the fragments sampled include purely non-lexical rules that do not conform to the rule constraints of Hiero, and rules that are not useful for translation.


Posing phrase movements as a prediction problem using contextual features modeled by maximum entropy-based classifier is superior to the commonly used lexicalized reordering model.

training this discriminative model using large-scale parallel corpus might be computationally expensive.


State-of-the-art statistical machine translation systems use large amounts of parallel data to estimate translation models.

parallel corpora are expensive and not available for every domain.


It is interesting to compare these weights to the perplexities of the correct decipherment measured using different n-gram orders (Table 5).

at this point we do not see any obvious connection between perplexities and weights w n , and leave this as a further research direction.


At the same time, the word @ •Ï Ç(thrombocytopenia) appears frequently in the disease domain and can be treated as a domain-specific term.

for such a simple sentence, current segmentation tools perform poorly.


For example, for the MWE china clay, the definition is kaolin, which includes neither of the components.

we find the component word clay in the definition for kaolin, as shown below.


For annotator noise modelling, we assume that a "ground truth" exists and that annotations are some noisy deviations from this truth.

for some settings these assumptions do not necessarily hold and often tasks can be anti-correlated.


Setting a larger rank allows more flexibility in modelling task correlations.

a higher number of hyperparameters may lead to overfitting problems or otherwise cause issues in optimisation due to additional non-convexities in the log likelihood objective.


we note the differences in experimental setting mean this comparison is perhaps not completely fair (different training sets).


We can obtain a gold DEP-DT by transforming a gold Rhetorical Structure Theory-based discourse tree (RST-DT).

there is still a large difference between the ROUGE scores of a system with a gold DEP-DT and a system with a DEP-DT obtained from an automatically parsed RST-DT.


The above formulation of the word similarity model can be interpreted as a mixture model in which w is similar to w if any of the context probabilities agrees.

to guard against false positives, we can alternatively reformulate it as a product of experts (Hinton, 1999), where Z(w) is a normalization constant.


Models 2-5 and the HMM-based model introduce additional components in order to capture word ordering and word fertility.


A more semantic approach resorts to training word alignments on semantic word classes (Ma et al., 2011).

the resulting alignments are only used to supplement the word alignments learned on lexical words.


After having generated for each word their vector representation, we use them as features for the annotated data to classify event roles.

event role fillers are not generally single words but noun phrases that can be, in some cases, identified as named entities.


Systems such as ReVerb, PATTY, OLLIE, and Exemplar have attracted much attention on English ORE.

few studies have been reported on ORE for languages beyond English.


Many NLP and IR applications, including selectional preference learning, commonsense knowledge and entailment rule mining, have benefited from ORE (Ritter et al., 2010).

most existing ORE systems focus on English, and little research has been reported on other languages.


We can see that, just like the case of using other feature alone, using the score of random walk alone is far from enough.

the first 5 candidates contain most of the correct answers.


However, it performs poorly without the character features (Column 2).

without the character features, our method (Column 4) works much better than the sequence labeling method.


Distant supervision has become the leading method for training large-scale relation extractors, with nearly universal adoption in recent TAC knowledge-base population competitions.

there are still many questions about the best way to learn such extractors.


In addition, they also used human annotated linguistic information as "latent" features in their work, which are similar to our implicit linguistic features.

the "latent" features that they used in their system are humanannotated, while the eventuality type and modality features in our system are predicted automatically.


For example, they excluded verbal expressions in Chinese that are translated into nominal phrases in English.

we kept all events in our data, and they can be realized as verbs, nouns, as well as words in other parts of speech.


There are two categories that are even less than 5%.

even though we only use some simple features, our model still beats the most frequent label baseline (around 35% accuracy) by a big margin, as shown in Table 4.


In fact, on the datasets with all events, joint learning with modality produces the highest accuracy among all approaches.

joint learning with eventuality is even worse than the baseline.


In the Chinese sentence, MaxEnt b classifies "讨 论(tao3lun4)" as "Future" because there is no grammatical indicator in the Chinese sentence implying that the "discussion" has already happened and it is reasonable to assume the "discussion" is in the near future.

with eventuality type "Episodic" and modality label "Actual", MaxEnt em classifies it as "Past" correctly, because episodic events tend to occur in the past and future events tend to get "Intended" or "Hypothetical" modality labels.


Populating Knowledge Base (KB) with new knowledge facts from reliable text resources usually consists of linking name mentions to KB entities and identifying relationship between entity pairs.

the task often suffers from errors propagating from upstream entity linkers to downstream relation extractors.


Information is often augmented or conveyed by non-textual features such as positioning, font size, color, and images.

traditionally, NER approaches rely exclusively on textual features and as a result could perform poorly in visually rich genres such as online marketing flyers or infographics.


To improve accuracy, CoTS combined this frequency signal with manually supplied constraints such as the functionality of the US presidency relation to scope the beginning and end of Nixon presidency.

the proposed system does not require constraints as input.


Prior work, CoTS, inferred end times by leveraging manually specified constraints, e.g., that there can only be one vice president at a time: the beginning of one signals the end of another (Talukdar 2012b).

such methods do not scale due to the amount of constraints that must be hand-specified.


Our ILP formulation takes around 8.5hrs.

mImLRE algorithm (Em-based) takes around 23hrs to converge.


This is because, hoffmann-ilp predicts significantly more nil labels.

nil labels are not part of the label-set in the P/R curves reported in the community.


Gesmundo and Henderson (2014) also consider the rankings between partial translation pairs as well.

they evaluate a partial translation through extending it to a complete translation by re-decoding, and thus they need many passes of decoding for many partial translations, while ours only need one pass of decoding for all partial translations and thus is much more efficient.


Of course, learning synchronous grammars from parallel data is a widely studied problem (Wu, 1997;Blunsom et al., 2008;Levenberg et al., 2012, inter alia).

there has been less exploration of learning rich non-terminal categories, largely because previous efforts to learn such categories have been coupled with efforts to learn derivation structures-a computationally formidable challenge.


It is these rules that allow the right translation to be preferred since the MLE chooses not to place the object of the sentence in the subject's span.

the spectral parameters seem to discriminate between these higherlevel rules better than EM, which scores spans starting with the first word uniformly highly.


(2008) present a Bayesian model for synchronous grammar induction, and place an appropriate nonparametric prior on the parameters.

their starting point is to estimate a synchronous grammar with multiple categories from parallel data (using the word alignments as a prior), while we aim to refine a fixed grammar with additional latent states.


1 For example, the gender of the employee Kay Mann was marked as unknown in their gender assignment.

in our work, we manually research and determine the gender of every core employee.


This is because the other features in fact make quite different predictions depending on gender and/or gender environment.

the content features (and in particular the lexical features) are so powerful on their own that the relative contribution of the gender-based features decreases again.


Twitter-LDA, which assumes a single tweet consists of a single topic, has been proposed and has shown that it is superior in topic semantic coherence.

twitter-LDA is not capable of online inference.


(2011) show that it works well at the point of semantic coherence of topics compared with LDA.

as with the case of LDA, Twitter-LDA cannot consider a sequence of tweets because it assumes that samples are exchangeable.


In Twitter-LDA, π is common for all users, meaning that the rate between background and topic words is the same for each user.

this assumption could be incorrect, and the rate could differ for each user.


Their model learns the transition parameters among topics by minimizing the prediction error on topic distribution in subsequent tweets.

the TM-LDA does not consider dynamic word distributions.


An online variational Bayes algorithm for LDA is also proposed (Hoffman et al., 2010).

these methods are based on LDA and do not consider the shortness of a tweet.


Similarly to Feldman and Peng (2013), out starting point is that idioms are semantic outliers that violate cohesive structure, especially in local contexts.

our task is framed as supervised classification and we rely on data annotated for idiomatic and literal expressions.


If a target query appears in a similar semantic context, the topics will be able to describe this query as well.

one might similarly apply LDA to a given query to extract query topics, and create the query vector from the query topics.


The time complexity of PTK is O(pρ where p is the largest subsequence of children that one wants to consider and ρ is the maximal out-degree observed in the two trees.

the average running time again tends to be linear for syntactic trees (Moschitti, 2006).


There are cleverer ways to reduce the complexity (e.g., see (Huang and Chiang, 2005) for three such ways).

since the efficiency of the algorithm did not limit us to produce k-best parses for larger k, it was not a priority in this work.


Since in our problem a pair of hypotheses h i , h j constitutes a data instance, we now need to define the kernel between the pairs.

notice that DISCTK only works on a single pair.


Note that more important relational features would be the subtree patterns extracted from the DT.

they are already generated by TKs in a simpler way.


These approaches achieve state-of-the-art performance mainly relying on morphosyntactic and lexical factors.


Hence, the system is able to correctly identify some mentions even in the presence of parsing or preprocessing errors.

as a result, IMSCoref has to process many spurious mentions, which makes learning more difficult.


They report preliminary results using the CoNLL scorer.

we think the coreference resolution system and the evaluation metric for coreference resolution are not suitable for bridging resolution since bridging is not a set problem.


Therefore it is difficult for the learning-based approach to learn effective rules to predict bridging links.

all learning-based systems tend to have higher recall but lower precision compared to the rule-based system.


The models are considerably better at resolving explicit referrals (both non-spatial and spatial) compared to implicit ones.

for locational referrals, the difference between the accuracy of implicit and explicit REs is significant (75.2% vs. 56.6% in media and 86.2% vs. 56.7% in places).


For example, the Rhetorical Structure Theory (RST) (Mann and Thompson, 1988) represents a discourse as a tree with phrases or clauses as elementary discourse units (EDUs).

rST ignores the importance of connectives to a great extent.


Example 1shows an explicit reason relation signaled by the discourse connective "particularly if" and an implicit result relation represented by the inserted discourse connective "so", with Arg1 in italics and Arg2 in bold.

as a connective and its arguments are determined in a local contextual window, it is normally difficult to deduce a complete discourse structure from such a connective-argument scheme.


First, the pair-wise classifier approaches learn a classifier on mention pairs (edges) (Soon et al., 2001;Ng and Cardie, 2002;Bengtson and Roth, 2008), and perform some form of approximate decoding or post-processing using the pair-wise scores to make predictions.

the pair-wise classifier approach suffers from several drawbacks including class imbalance (fewer positive edges compared to negative edges) and not being able to leverage the global structure (instead making independent local decisions).


Indeed, several of the approaches that have achieved state-of-the-art results on OntoNotes fall under this category Björkelund and Kuhn, 2014).

their efficiency requirement leads to a highly nonrealizable learning problem.


Specifically, when learning F prune in the worst case there can be ambiguity about which of the non-optimal actions to retain, and for only some of those an effective F score can be found.

we observe a loss decomposition in terms of the individual losses due to F prune and F score , and develop a stage-wise learning approach that first learns F prune and then learns a corresponding F score .


We essentially employ the same features as in the Easyfirst system.

we provide some highlevel details that are necessary for subsequent discussion.


The simplest method is to use a bag-of-words representation derived from the text description.

this scheme disregards the ordering of words and the finer nuances of meaning that evolve from composing words into sentences and paragraphs.


One solution to this problem is to approximate Q(s, a) using a parametrized function Q(s, a; θ), which can generalize over states and actions by considering higher-level attributes (Sutton and Barto, 1998;Branavan et al., 2011a).

creating a good parametrization requires knowledge of the state and action spaces.


The simplest method to create these minibatches from the experience memory D is to sample uniformly at random.

certain experiences are more valuable than others for the agent to learn from.


The game has an additional quest of reaching a secret tomb.

this is a complex quest that requires the player to memorize game events and perform high-level planning which are beyond the scope of this current work.


Interestingly, DIST2REF does also show some cultural effects in its geolocation errors: For example, some Pacific island states with lesser-known identities (e.g., Nauru and French Polynesia) are placed in the Indian Ocean, where we find the perhaps prototypes of beautiful islands, like Seychelles and Mauritius; also, Central American countries (such as Panama, El Salvador, and Nicaragua) move towards their "cultural center of gravity", South America.

this kind of cultural bias is much more prominent in the original WORD2VEC distributional representation.


The broader goal of getting at referential information with distributional semantics is shared with Herbelot (2015).

the specific approach is different, as she constructs vectors for individual entities (literary characters) by contextualizing generic noun vectors with distributional properties of those entities.


The corresponding grammars for recognizing and producing graphs are more flexible and powerful than tree grammars.

because of their high complexity, graph grammars have not been widely used in NLP.


Recently, along with progress on graph-based meaning representation, hyperedge replacement grammars (HRG) (Drewes et al., 1997) have been revisited, explored and used for semantic-based machine translation (Jones et al., 2012).

the translation process is rather complex and the resources it relies on, namely abstract meaning corpora, are limited as well.


ITGs recognize only the binarizable permutations, which is a major restriction when used on the data: there are many nonbinarizable permutations in actual data (Wellington et al., 2006).

our PETs are obtained by factorizing permutations obtained from the data, i.e., they exactly fit the range of prime permutations in the parallel corpus.


Therefore, we accept the rewritten sentence.

when the subject phrase is long and the object phrase is short, a swap may not reduce delay.


This work is also related to preprocessing reordering approaches (Xu et al., 2009;Collins et al., 2005;Galley and Manning, 2008;Hoshino et al., 2013;Hoshino et al., 2014) in batch MT for language pairs with substantially different word orders.

our problem is different in several ways.


@RachOrange (California) This tweet is a positive example from the USA toward Pakistan.

a typical sentiment classifier misclassifies this as negative because miss and sad express sadness.


Much of the existing work focuses on annotating a single Twitter message (tweet).

information in Twitter is rarely digested in isolation, but rather in a collective manner, with the adoption of special mechanisms such as hashtags.


While the content-based methods (Meij et al., 2012;Guo et al., 2013;Fang and Chang, 2014) consider tweets independently, the graph-based methods (Cassidy et al., 2012;Liu et al., 2013) use all related tweets (e.g., posted by a user) together.

most of them focus on entity mentions in tweets.


The external information derived from the tweets is largely ignored.

we exploit both context information from the microblog and Wikipedia resources.


In that work, the author proposes graph-based models and achieves fair amount of improvement.

to the best of our knowledge, no previous work of this task tries to focus on summarization beyond pure sentence extraction.


However, to the best of our knowledge, no previous work of this task tries to focus on summarization beyond pure sentence extraction.

cross-language summarization can be seen as a special kind of machine translation: translating the original documents into a brief summary in a different language.


Since the inception of BLEU, evaluation of automatic metrics in MT has been by correlation with human assessment.

in summarization, over the years since the introduction of ROUGE, summarization evaluation has seen a variety of different methodologies applied to evaluation of its metrics.


As described in Section 2, when used for the evaluation of metrics, linguistic quality is commonly omitted, however, with metrics only assessed by the degree to which they correlate with human coverage scores.

we include all available human assessment data for evaluating metrics.


As mentioned earlier, there have been numerous studies that used data from the public Twitter feeds.

none of the datasets in those studies focused on tweets and related articles linked to these tweets.


We found that it did not match with the titles.

even though there are no exact matches, there might still be matches where the tweet is a slight modification of the headline of the article, and can be measured using a partial match measure.


A further approach removed the requirement of seed lexicons, and induced lexicons using bilingual spaces spanned by multilingual probabilistic topic models (Vulić et al., 2011;Liu et al., 2013;Vulić and Moens, 2013b).

these models require document alignments as initial bilingual signals.


The MEN dataset  that Kiela and Bottou (2014) evaluate on explicitly measures word relatedness.

the current lexicon learning task seems to require something else than relatedness: whilst a chair and table are semantically related, a translation for chair is not a good translation for table.


This could explain why we did not see increased performance on the bilingual lexicon induction task with additional layers.

the increase in performance on the relatedness task is relatively minor, and further investigation is required into the utility of the additional layers for relatedness tasks.


The ever increasing user generated content has always been motivation for sentiment analysis research, but majority of work has been done for English Language.

in recent years, there has been emergence of increasing amount of text in Hindi on electronic sources but NLP Frameworks to process this data is sadly miniscule.


Joint models of relevance and subjectivity have a great benefit in that they have a large degree of freedom as far as controlling redundancy goes.

conventional two-stage approach Pang and Lee (2004), which first generate candidate subjective sentences using min-cut and then selects top subjective sentences within budget to generate a summary, have less computational complexity than joint models.


In contrast, conventional two-stage approach Pang and Lee (2004), which first generate candidate subjective sentences using min-cut and then selects top subjective sentences within budget to generate a summary, have less computational complexity than joint models.

two-stage approaches are suboptimal for text summarization.


For example, when we select subjective sentences first, the sentiment as well information content may become redundant for a particular aspect.

when we extract sentences first, an important subjective sentence may fail to be selected, simply because it is long.


Much fine-grained analysis is span or aspect based (Yang and Cardie, 2014;Pontiki et al., 2014).

this work contributes to entity/event-level sentiment analysis.


While the targets in aspect-based sentiment analysis are often entity targets, they are mainly product aspects, which are a predefined set.

3 the target in the entity/event-level task may be any noun or verb.


Previously, we also propose a set of sentiment inference rules and develop a rule-based system to infer sentiments .

the rule-based system requires all information regarding explicit sentiments and +/-effect events to be provided as oracle information by manual annotations.


Since the exact boundaries of the spans are hard to define even for human annotators (Wiebe et al., 2005a;Yang and Cardie, 2013), the target span in MPQA 2.0 could be a single word, an NP or VP, or a text span covering more than one constituent.

in MPQA 3.0, each target is anchored to the head of an NP or VP, which is a single word.


This shows the results from span-based sentiment analysis systems do not provide enough accurate information for the more fine-grained entity/eventlevel sentiment analysis task.

pSL1 achieves much higher accuracy than the baselines.


The benefit of the word2vec text feature is clear when moving from high-level categories to original terms from descriptions, where it consistently improves the mean rank (up to 25%).

the indicator vectors resulted in a less significant improvement, if not worse performance, when using the sparse original terms.


For concave optimization problems like IBM Model 1, we have guarantees on the convergence of optimization algorithms such as Expectation Maximization (EM).

as was pointed out recently, the objective of IBM Model 1 is not strictly concave and there is quite a bit of alignment quality variance within the optimal solution set.


Summarizing (Moore, 2004), we note that this work improves substancially upon the classical IBM Model 1 by introducing a set of heuristics, among which are to (1) modify the lexical parameter dictionaries (2) introduce an initialization heuristic (3) modify the standard IBM 1 EM algorithm by introducing smoothing (4) tune additional parameters.

we stress that the main concern of this work is not just heuristicbased empirical improvement, but also structured learning.


They use these vectors as input to a logistic regression classifier and achieve state-of-the-art performance on sentiment classification of movie reviews.

they did not consider the effect of this model modification directly on the task of language modelling.


Continuously retraining the model and adjusting parameters can be very time-consuming compared to a simple feedforward process through the network.

extra computation is also needed when using a hidden vector of size M , as opposed to using a smaller value.


Usually unsupervised knowledge sources are used to form semantic codes of the labels that helps us to generalize to unseen labels.

there are also different ways to express the same meaning, and similarly, most of them can not be included in the training set.


(2014), the authors presented a simple technique for speeding up feed-forward embedding-based neural network models, where the dot product between each word embedding and part of the first hidden layer are pre-computed offline.

this technique cannot be used for hidden layers beyond the first.


Perhaps most surprisingly, the additive function performs as well as the max function, despite the fact that it provides no additional modeling power compared to a 1-layer network.

it does allow the model to generalize better than a 1-layer network by explicitly tying together two or three hidden nodes from each node in the output layer.


Fortunately, most structured margin objectives are convex, so a range of optimization methods with similar theoretical properties are available -in short, any of these methods will work in the end.

in practice, how fast each method converges varies across tasks.


In such cases, most learning methods work fairly well.

when models use real-valued features, learning may involve determining a more delicate balance between features.


The dual methods were particularly sensitive to these hyperparameters, performing poorly if they were not chosen carefully.

performance for the primal methods remained high over a broad range of values.


In order to reduce noise in training data, most natural language crowdsourcing annotation tasks gather redundant labels and aggregate them into an integrated label, which is provided to the classifier.

aggregation discards potentially useful information from linguistically ambiguous instances.


Percentage Agreement In this paper, we follow Beigman Klebanov and Beigman (2014) in using the nominal agreement categories Hard Cases and Easy Cases to separate instances by item agreement.

unlike Beigman Klebanov and Beigman (2014) who use simple percentage agreement, we calculate item-specific agreement via Krippendorff (1970)'s α item agreement 4 , with Nominal, Ordinal, or Ratio distance metrics as appropriate.


did not significantly outperform Integrated.

highAgree does outperform Integrated on 4 or the 5 tasks, especially for hard Cases: hard Case improvements for Biased Language and POS Tagging, and Affective Text, and overall improvements for RTE, POS Tagging, and Affective Text were significant (Paired TTest, p < 0.05, for numerical output, or McNemar's Test 10 (McNemar, 1947), p < 0.05, for nominal classes).


As a consequence, we need our gold ranking to define an order on all the word pairs.

this also means that we somehow need to order completely unrelated word pairs; for example, we have to decide whether (dog, cat) is more similar than (banana, apple).


All embeddings perform better than guessing, indicating that there is at least some coherent structure captured in all of them.

the best performing embeddings at this task are TSCCA, CBOW and GloVe (the precision mean differences were not significant under a random permutation test), while TSCCA attains greater precision (p < 0.05) in relation to C&W, H-PCA and random projection embeddings.


These results are in contrast to the direct comparison study, where the performance of TSCCA was found to be significantly worse than that of CBOW.

the order of the last three embeddings remains unchanged, implying that performance on the intrusion task and performance on the direct compari- son task are correlated.


Extrinsic evaluations use embeddings as features in models for other tasks, such as semantic role labeling or part-of-speech tagging (Collobert et al., 2011), and improve the performance of existing systems (Turian et al., 2010).

they have been less successful at other tasks such as parsing (Andreas and Klein, 2014).


LDA is an unsupervised model-it requires no annotation-and discovers, without any supervision, the thematic trends in a text collection.

lDA's lack of supervision can lead to disappointing results.


With SparseLDA, inferring LDA models over large topic spaces becomes tractable.

existing methods for incorporating prior knowledge use conventional Gibbs sampling, which hinders inference.


1 Therefore, the posterior topic assignments v and w will be correlated.

if v and w are uncorrelated, nothingother than the Dirichlet's rich get richer effectprevents the topics from diverging.


their topic probabilities are correlated.

a cannot-link relation between two words indicates that these two words are not topically similar, and they should not both be prominent within the same topic.


Path queries on a knowledge graph can be used to answer compositional questions such as "What languages are spoken by people living in Lisbon?".

knowledge graphs often have missing facts (edges) which disrupts path queries.


For the small dataset, it is clear that the data is insufficient for the model to learn a good tagset mapping, especially for a morphologically rich language like Czech.

with more data, the model is better able to learn the tagset mapping as part of joint training.


Compared with N-gram models, syntactic models capture rich structural information, and can be more effective in improving the fluency of large constituents, long-range dependencies and overall sentential grammaticality.

syntactic models require annotated syntactic structures for training, which are expensive to obtain manually.


The syntactic model requires that the training sentences have syntactic dependency structure.

only the WSJ data contains goldstandard annotations.


It conforms to the intuition that syntactic quality affects the fluency of surface texts.

the influence is not huge, the BLEU scores decrease by 1.0 points as the parsing accuracy decreases from 88.10% to 57.31% The influence of parsing accuracy of the training data on cross-domain word ordering is measured by using the same training settings, but testing on the WPB and SANCL test sets.


This suggests that it is possible to use large automatically-parsed data to train syntactic models.

when the training data scale increases, syntactic models can become much slower to train compared with Ngram models.


This again shows the effect of syntactic quality of the training data.

as the scale of automaticallyparsed AFP data increases, the performance of the  syntactic model rapidly increases, surpassing the syntactic model trained on the high-quality WSJ data.


This can be explained by the local scoring nature of the N-gram model.

the syntactic model makes less long-range distortions, which can suggest better sentence structure.


The syntactic model performs better in most constituent labels.

the N-gram model performs better in WHPP, SBARQ and WHNP.


It would be overly expensive to obtain a human oracle on discusses.

according to Papineni (2002), a BLEU 7 For the combined model, we used the WSJ training data for training, because the syntactic model is slower to train using large data.


Generally the models are good at picking out key words from the input, such as names and places.

both models will reorder words in syntactically incorrect ways, for instance in Sentence 7 both models have the wrong subject.


Since DP is the extension of finite mixture models to the nonparametric setting, the appropriate tool for nonparametric topic models is HDP.

both LDA and HDP are normally suitable for long documents.


As expected, for pairs 1 → 2, 3 → 4, and 5 → 6, all the scores are below their corresponding upper bounds from the in-domain setting in Table 3.

for pair 7 → 8, the QWK score for domain adaptation with 100 target essays outperforms that of the in-domain, albeit only by 0.4%.


For example, common instances of TE are rephrases or summarizations of a sentence, however they cannot serve to support a claim within a discussion, as they merely repeat it (Table 1, S6).

an anecdotal story may have strong emotional impact that will effectively support a claim during a discussion, although the truth of the claim cannot be inferred from such evidence.


In addition, some works based on machinelearning techniques, used the same topic in training and testing (Rosenfeld and Kraus, 2015;Boltužić anď Snajder, 2014), relying on features from the topic itself in identifying arguments.

here, we focus on detecting an essential constituent of an argumentthe evidence -rather then detecting whole arguments, or detecting other argument parts like claims Lippi and Torroni, 2015).


Since, to the best of our knowledge, this is the first work to address CDED, there is no prior-art to compare our results to.

to ensure that this task is indeed empirically different from related tasks, and demands a specialized pipeline to handle, we compare with two baselines that are often used in related tasks.


The classification of query chains is performed by support vector machines, and its training data is generated in a supervised fashion by manual inspection and annotation.

we do not manually annotate any of our training data.


These tasks use automatic metrics to determine the quality of the participating systems.

these efforts pale in comparison to competitions organized in other fields, e.g.


Our experiments show that this method does improve click prediction performance.

this method has several potential weaknesses.


In the latest SemEval campaign (UzZaman et al., 2013), the rule-based Hei-delTime (Strötgen and Gertz, 2010) out performed machine-learning and hybrid counterparts by a large margin.

statistical systems obtained promising results with respect to temporal entity extraction.


We studied the effect of corpus size by training a model using a portion of the clinical training data equivalent in size to that of FTB (marked clin-in Table 3).

the ATC corpus was too small to train any usable models (results not shown).


State-of-the-art approaches rely on word vector representations with TF-IDF weights (Salton and Buckley, 1988).

expanding the seed set by relying on TF-IDF representations to find similar instances has limitations, since the similarity between any two relationship instance vectors of TF-IDF weights is only positive when the instances share at least one term.


In Table 1, for example, both the annual, Milankovitch and continuum temperature variability and annual temperature between 1958 and 2010 are generalised to annual temperature.

many generalised variables are unique and thus serve no purpose in relating variables.


Since many sentences are long, complex and domainspecific, it comes as no surprise that the parser often fails to correctly resolve well-known ambiguities in coordination and PP-attachment.

with pattern matching on strings and/or POS tags instead of syntax trees, determining boundaries of variables would be problematic.


Generalisation by tree pruning appears to work quite well as long as the parse is correct.

pruning by itself is insufficient and should be supplemented with other methods.


The experiments illustrate that if we are lucky enough to have KB tags, they improve NER.

the models use all possible KB tags and should be considered an upper bound.


While such systems aim for extraction coverage, and because they operate in an ontologyfree setting, they don't directly address the problem of improving knowledge density in ontological KGs such as NELL.

oIE extractions provide a suitable starting point which is exploited by ENTICE.


We can see that our model still benefits from the randomly sampled negative examples, which may help our model learn to refine the margin between the positive and negative examples.

with similar amount of negative examples, treating the reversed dependency paths from objects to subjects as negative examples can achieve a better performance (85.4% F1), improving random samples by 1.9%.


Of course, exceptions exist, e.g., TipSem for English and Spanish (Llorens et al., 2010), and Hei-delTime  even covers 13 languages.

it required a lot of manual effort to extend HeidelTime, and researchers of different institutes and countries have been involved for German (Strötgen and Gertz, 2011), Dutch (van de Camp and Christiansen, 2012), Spanish , French (Moriceau and Tannier, 2014), Croatian (Skukan et al., 2014), Vietnamese and Arabic , Italian (Manfredi et al., 2014), Chinese (Li et al., 2014), Russian, Estonian, and Portuguese.


While there have been earlier approaches for automatic extensions of temporal taggers to further languages (Saquete et al., 2004;Negri et al., 2006;Spreyer and Frank, 2008), these were limited to a few languages and the results were considered less successful, in particular for the normalization subtask.

angeli and Uszkoreit (2013) presented an approach to language-independent parsing of temporal expressions, however, addressing only the normalization and not the extraction subtask.


Note that in contrast to the original rules, these rules do neither contain pos constraints nor any English terms to guarantee language independence.

fuzzy pattern matching is allowed at the end of some patterns to try to take care of morphology-rich languages.


This is the case for new languages and domains, the task we face in this paper.

training embeddings for Chinese is not straightforward: Chinese is not word segmented, so embeddings for each word cannot be trained on a raw corpus.


Since one entity belongs to multiple types, relation schemas with general types will be ranked higher.

two different schemas may share the same support.


For example, we can learn that the type person subsumes types such as actor, politician and deceased person.

strict set inclusion doesn't always hold in the knowledge base.


In GM, a fixed set of combination weights (i.e., ω) are learned to optimize the overall performance for all entity-document pairs.

the best combination strategy for a given pair is not always the best for the others since both the documents and entities are heterogeneous.


Moreover, topic LDTM generally performs better than src LDTM in both scenarios, which meets our expectation because topic-based features have far more dimensions than source-based features.

even if source-based feature vector holds a few dimensions (10 in our experiments), src LDT improves the precision on the basis of GM.


Transfer learning is utilized to transfer the keyword importance learned from training pairs to query pairs (Zhou and Chang, 2013).

some highly supervised methods require training instances for each entity to build a relevance model, limiting their scalabilities.


Allowing patients access to their own electronic health records (EHRs) can enhance medical understanding and provide clinical relevant benefits (Wiljer et al., 2006), including increased medication adherence (Delbanco et al., 2012).

eHR notes present unique challenges to the average patients.


Other related work that reduces long queries includes ranking all subsets of the original query (Kumaran and Carvalho, 2009).

typical EHR notes are longer than the passages and verbose queries in these systems, which makes the graphical model and other learning based models less efficient.


Wikipedia, especially the Medicine category, is an appealing resource for such information as the human curated links in them are naturally concepts that are important.

the number of articles in the Medicine category outnumbers our EHR notes substantially, we therefore restricted the Wikipedia articles to the Diabetes category.


This can be attributed to the fact that the Wikipedia articles outnumbered the EHR data by 7 times.

this data helped improve the coverage of the key concepts.


A straightforward approach is to crawl bilingual documents from the Web for use as training data.

because most documents on the Web are written in one language, it is not always easy to collect a sufficient number of multilingual documents, especially those involving minor languages.


Although both GCCA and CCA show improved performance as the sample size  of [train-E/J] increases, not surprisingly, GCCA is gradually overtaken by CCA when we have enough samples to learn the relevance between English and Japanese texts directly.

accuracies of image-mediated learning in the cases when [train-E/J] is scarce are higher than CCA baseline.


These classifiers can be seen as a baseline for comparisons.

the remaining SVM clearly yields the best results among the employed algorithms.


Nevertheless, they are not good enough for reliable predictions.

the aggregated classification data accurately predict whether the majority of banks will increase or decrease their Tier 1 capital ratio in the following year: for 12 out of 13 years, the algorithm correctly predicts the direction of the T1 evolution.


Hence, the sentiment scores could be used in regression models for predicting the T1 evolution.

the results are only meaningful if the figures are aggregated by year.


E.g., while the product review and the hotel review in Figure 1 cover opinions on several aspects, no deliberate structure is found in their argumentation.

the ex-cerpt of the more professional movie review shows that this is not always the case.


Our hypothesis is that similar sentiment flows are used across domains of web reviews to express the same global sentiment.

because of the domain differences described in Section 3, we do not expect that the original sentiment flows of web reviews generalize well.


Intuitively, the interaction between entity boundaries and sentiment classes might not be as strong as that between more closely-coupled sources of information, such as word boundaries and POS (Zhang and Clark, 2008), or named entities and constituents (Finkel and Manning, 2009), for which joint models significantly outperform pipeline models.

there do exist cases where entity boundaries and sentiment classes reinforce each other.


As a result, the neural model can better capture patterns that do not occur in the training data.

the discrete model is based on manually defined binary features, which do not fire if not contained in the training data.


tity recognition, the pipeline can be a favorable choice.

although useful for some joint sequence labeling task (Ng and Low, 2004), the collapsed task does not seem to address the joint sentiment task as effectively.


A fundamental issue in opinion mining is to search a corpus for opinion units, each of which typically comprises the evaluation by an author for a target object from an aspect, such as "This hotel is in a good location".

few attempts have been made to address cases where the validity of an evaluation is restricted on a condition in the source text, such as "for traveling with small kids".


For example, those who intend to improve the quality of hotel A may investigate representative values for "Aspect" in the units satisfying "Target=hotel A & Polarity=negative", while those who look for accommodation may collect the opinion units for one or more candidate hotels and investigate the distribution of values for "Polarity" on an aspect-by-aspect basis.

in the above example (1), the evaluation for hotel A ("a reasonable price") is valid for "if you take a family trip with small kids", and thus it is not clear whether this evaluation is valid irrespective of the condition.


For example, the distribution of positive and negative opinions can be available on a category-bycategory basis.

in this paper we focus only on the identification for CFOs and leave the semantic classification future work.


Candidate categories include demographic and psychographic attributes for target users (e.g., age and hobby) and situations of target users (e.g., purpose, time, and place).

we leave the classification for U-CFOs future work.


y n , where y i ∈ {BU, IU, BC, IC, Other, T arget, Aspect, OpinionW ord}.

because an opinion unit in an input sentence has been identified in advance, the task is a quinary classification with respect to y i ∈ {BU, IU, BC, IC, Other}.


These methods compared the context of the entities and their reference pages in the KBs through a similarity measurement.

we tested 32 different names of General Motors (GM) car brands, and only four of the brands exist in Wikipedia.


Men-tionRank, the only state-of-the-art method for T-ED, is a graph-based model that focuses on a small set of entities (e.g., 50 or 100 entities) and conducts experiments on thousands of documents .

the graph has a quadratic growth as the number of documents increases.


In the graph, the context similarity between documents is computed and used as the edges normally, where the width of the context window that surround the target entity in the document is typically chosen to be 10, 20 or 50 words.

the documents considered here are limited in length, and a user seldom changes the topic in so few words; thus, we regard the entire document as the context of its target entities.


On one hand, the number of layers l declines sharply when η is less than 0.1, this indicates little difference in the trust level of documents in the propagation.

our method has no effect on the vertices outside of the graph, so the performance is directly proportional to the graph coverage rate.


EM yields precise archiving results because the constraint conditions are helpful to reduce uncertainty in string matching.

fM-based archiving is able to match entity name mentions with various forms, and thus it achieves higher recall.


In total there are 1,851 gold standard relevant documents available for the evaluation of entity archiving.

the data is far from enough because it only covers a small portion of all relevant documents in the pool.


The reasons are as following: 1) RM gives greater weights to the highfrequency words, and 2) popular slices are of much greater public interest and hence frequently mentioned in relevant documents.

some entities not only share similar names but similar popular slices, such as the religious vocation of different church scientologists.


Another observation is that in all the four figures, the performance of the stochastic ListNet methods increases with more samples of the object lists.

if there are too many samples, the performance starts to decrease.


For stochastic ListNet, the performance improves with k increases.

to the conventional ListNet, this improvement propagates to the results on the test data.


These mention at most 3 variables each and thus have relatively manageable groundings.

as discussed in the next section, ER-MLN can fail on questions that have distinct entities with similar string representations (e.g.


Therefore, our approach is corpus-level: We infer the types of an entity by considering the set of all of its mentions in the corpus.

named entity recognition (NER) is context-level or sentence-level: NER infers the type of an entity in a particular context.


• We address the problem of corpus-level entity typing in a knowledge base completion setting.

to other work that has focused on learning relations between entities, we learn types of entities.


That is clearest for tail entities -where one bad context can highly influence the final decision -and for tail types, which CM was not able to distinguish from other similar types.

the good results of the simple JM confirm that the score distributions in CM do help.


This negatively affects evaluation measures for many entities.

the resulting types do not have a balanced number of instances.


FREE-BASE) improves Web extractions by predicting their reliability.

in both cases the main objective is distantly supervised extraction from unstructured text, rather than KB unification.


Given KB i ∈ K U , our disambiguation module ( Figure  2) takes as input its set of unlinked triples T i and outputs a set T S i ⊆ T i of disambiguated triples with subject-object pairs linked to S. The triples in T S i , together with their corresponding entity sets and relation sets, constitute the redefined KB S i which is then added to K S .

applying a straightforward approach that disambiguates all triples in isolation might lead to very imprecise results, due to the lack of available context for each individual triple.


After disambiguation (Section 5) each KB in K is linked to the unified sense inventory S and added to K S .

each KB S i ∈ K S still provides its own relation set R S i ⊆ R i .


The distribution of entities over relations is however very   skewed, with 80.33% of the triples being instances of the generalizations relationship.

rEVErB contains a highly sparse relation set (1,299,844 distinct relations) and more than 3 million distinct entities.


For the MT RNN and BOW + ME classifiers, the additional training data primarily benefits recall, particulary for the General domain.

the ST RNN sees an improvement in precision for all domains.


Name errors have a similar character to OOV erors in that they often have anomalous word sequences in the region of the OOV word (examples 1 and 2), which is why the OOV posterior is so useful.

too much reliance on the OOV posterior leads to wrongly detecting general OOV errors as name errors ('lizards' and 'zombies' in example 2) and missed detection of name errors where the confusion network cues indicate a plausible hypothesis ('omaha' in example 3).


To motivate this method, recall that one important property of a sieve-based approach is that later sieves can exploit earlier sieves' decisions when making their own decisions.

our first method of using sieves makes limited use of the decisions made by earlier sieves.


These current approaches have largely assumed that characters can be reliably identified in text using standard techniques such as Named Entity Recognition (NER) and that the variations in how a character is named can be found through coreference resolution.

such treatment of character identity often overlooks minor characters that serve to enrich the social structure and serve as foils for the identities of major characters (Eder et al., 2010).


(2013) propose an alternate approach for identifying speaker references in novels, using a probabilistic model to identify which character is speaking.

to account for the multiple aliases used to refer to a character, the authors first manually constructed a list of characters and their aliases, which is the task proposed in this work and underscores the need for automated methods.


While novels written before 1850 had slightly more characters on average, this effect may be due to the smaller number of works available from this period.

our finding raises many questions about whether the social networks for these characters obey similar trends in their size and density.


While DPMM achieved some promising results, it can still sometimes produce unsatisfactory topic clusters due to its unsupervised nature.

people often have prior knowledge about what potential topics should exist in a given text corpus.


In our previous works, we have investigated several different topological structures (tree and directed acyclic graph) to recursively model the semantic composition from the bottom layer to the top layer, and applied them on Chinese word segmentation (Chen et al., 2015a) and dependency parsing (Chen et al., 2015b) tasks.

these structures are not suitable for modeling sentences.


One common problem among most of them is that different results are obtained depending on the cluster initialization, suggesting that some clusters are unstable or weak.

there is no obvious way to effectively and efficiently evaluate the quality of clusters.


Other features we implemented include the last word of the section, and a binary feature indicating whether the section title contains a year which is included between the date of birth and date of death of the person of interest.

both pieces of information led to a performance drop on the development set, so we did not include them in the final feature set.


We can observe that the number of possible alignments per word problem of our algorithm is much smaller than .

the number of all the false alignments is still 80K.


2 http://en.wikipedia.org/wiki/ Metaphone which in turn serves as grouping words of similar sounds (lexical variations) to one code.

most of the schemes are designed for English and European languages and are limited when apply to other family of languages like Urdu.


Traditionally, CCR and NEL have been addressed separately.

such approaches miss out on the mutual synergies if CCR and NEL were performed jointly.


NEL methods often harness the semantic similarity between mentions and entities and also among candidate entities for different mentions (in Wikipedia or other KBs) for contextualization and coherence disambiguation (Hoffart et al., 2011;Milne & Witten, 2008;Kulkarni et al., 2009;Ratinov et al., 2011).

in the absence of CR mention groups, NEL has limited context and is bound to miss out on certain kinds of difficult cases.


Recently, several joint models have been proposed for CR-NER (Haghighi & Klein, 2010;Singh et al., 2013), CR-NEL (Hajishirzi et al., 2013), and NER-CR-NEL (Durrett & Klein, 2014).

to the best of our knowledge, no method exists for jointly handling CCR and NEL on large text corpora.


CCR alone would likely miss the coreference relation between Logan (Doc 1) and its alias Wolverine (Doc 2), leaving NEL with the difficult task of disambiguating "Logan" in a document with sparse and highly ambiguous context (Doc 1).

nEL alone would likely map Australia (Doc 3) to the country (not the movie) and could easily choose the wrong link for mention "Hugh".


Similar to the behavior induced by δ s , we observe that a high τ limits entity linking and possible KB feature inclusion, while an extremely low value (near to zero) allows for noisy feature incorporation -both situations leading to lowered CCR efficiency.

since τ prevents gross mis-alignment of mentions to KB entities, a wide range of small value (0.1 − 0.35) is seen to provide comparable performance.


Hence, CCR cannot be efficiently tackled by simply employing CR methods on a "super-document".

harnessing of non-local mention features (via CCR) and efficient detection of new mentions using link validation enables C3EL to achieve a gain of around 5% in NEL compared to others (see Table 8(b)).


While approaches on addressing these issues exist, current algorithms typically suffer from high time complexity (Finkel and Manning, 2009) and are therefore difficult to scale to large datasets.

the problem of designing efficient and scalable models for mention extraction and classification from natural language texts becomes increasingly important in this era where a large volume of textual data is becoming available on the Web every day -users need systems which are able to scale to extremely large datasets to support efficient semantic analysis for timely decisonmaking.


• We might assist an online aligner by permuting our n segment pairs to place shorter, less ambiguous ones at the top.

we would have to communicate the permutation to the decompressor, at a prohibitive cost of log 2 (n!


Li and Hovy (2014) propose a neural network coherence model which employs distributed sentence representation and then predict the probability of whether a sequence of sentences is coherent or not.

to the methods mentioned above which learn the word relationship in or between the sentences separately, we propose a hierarchical recurrent neural network language model (HRNNLM) to capture the word sequence across the sentence boundaries at the document level.


2014propose a RNN Encoder-Decoder which is a joint recurrent neural network model at the sentence level as conventional SMT decoder does.

at the discourse level, there is little report on applying DNN to boost the translation result of a document.


Neural networks have been shown to improve performance across a range of natural-language tasks.

designing and training them can be complicated.


For example, neural language models and joint language/translation models improve machine translation quality significantly (Vaswani et al., 2013;Devlin et al., 2014).

neural networks can be complicated to design and train well.


Both have a sharp tip at the origin that encourages all the parameters in a row to become exactly zero.

this also means that sparsity-inducing regularizers are not differentiable at zero, making gradient-based optimization methods trickier to apply.


Ideally, what one then wants is to learn a function h : where X E n is the domain of instances representing a collection of EDUs for each dialogue and Y G is the set of all possible SDRT graphs.

given the complexity of this task and the fact that it would require an amount of training data that we currently lack in the community, we aim at the more modest goal of learning a function where the domain of instances X E 2 represents features for a pair of EDUs and Y R represents the set of SDRT relations.


Note, that the EG model with equal weighting scores slightly better than the one with optimized weighting for German but not for English.

this difference is not significant (p>0.5) for both languages, which indicates that the search for an optimal weighting is not necessary for the attachment task.


Studies have shown that such tasks can benefit from an explicit alignment component (Hickl and Bensley, 2007;Sultan et al., 2014b;Sultan et al., 2015).

alignment is still an open research problem.


The graph obtained by HARPY consists of around 600,000 hypernymy links for around 20,000 relational phrases.

the final graph was not evaluated for precision; rather, the evaluation was instead concentrated on the alignment between verb senses and relations.


"married to" and "relative of" for P 1 and P 2 .

unlike previous approaches such as Markov logic networks, the atoms in each logical rule take values in the [0,1] continuous domain.


Relations with semantic types were also used in typed entailment graphs (Berant et al., 2011).

the type hierarchy was not considered there, which prevented from creating links between two relations with different semantic types.


1, when the implicit object of "bake" is connected to the output of the "lay" action, it is inferred to be of type f ood since that is what is created by the "lay" action.

when the implicit P P argument of "bake" is connected to the output of the "preheat" action, it is inferred to be a location since "preheat" does not generate a food.


spans that denote the output of actions (e.g, 'batter," "banana mixture") should have low probability.


Data-driven extraction of cooking knowledge has been explored in the context of building a cooking ontology (Gaillard et al., 2012;Nanba et al., 2014).

our work induces probabilistic cooking knowledge as part of unsupervised learning process for understanding recipes.


4 Given a window size of n words around a word w, the skip-gram model predicts the neighboring words given the current word.

the CBOW model predicts the current word w, given the neighboring words in the window.


Two lines of research are directly relevant to our work: sarcasm detection in Twitter and application of distributional semantics, such as word embedding techniques to various NLP tasks.

to current research on sarcasm and irony detection (Davidov et al., 2010;Riloff et al., 2013;Liebrecht et al., 2013;Maynard and Greenwood, 2014), we have introduced a reframing of this task as a type of word sense disambiguation problem, where the sense of a word is sarcastic or literal.


Score Contrast (f ox, f lying f ox)) is 1.10, which is higher than the average collective contrastive score, 0.32.

the true taxonomic relation between 'bat' and 'flying fox' is not identified by the baseline, mainly due to the rare mention of this relation in the Web.


On the other hand, the true taxonomic relation between 'bat' and 'flying fox' is not identified by the baseline, mainly due to the rare mention of this relation in the Web.

our proposed method can recognize this relation because of two reasons: (1) 'flying fox' has many synonyms such as 'fruit bat', 'pteropus', 'kalong', and 'megabat', and there are much evidence that these synonyms are kinds of 'bat' (i.e.


For example, in Animal domain, our method identifies 'wild sheep' as a hyponym of 'sheep', but in WordNet, they are siblings.

many references 10 11 consider 'wild sheep' as a species of 'sheep'.


In its simplest form, the relations will suggest if the entities are linked together or not.

the underlying semantics of such relations can be deep such as the one in clue 5 of Problem.1.


The goal is to identify the relation possDif f (E1, E2, E3), where E1, E2, E3 are constituents having a nonnull class value.

instead of identifying posDif f (E1, E2, E3) directly, first the relation  3 ) } are identified, the extraction module will infer that posDif f (E1, E2, E3) holds.